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Platform for Interactive analysis and Visualization Of Transcriptomics data
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PIVOT: Platform for Interactive analysis and Visualization Of Transcriptomics data

About this package

This program is developed based on the Shiny framework, a set of R packages and a collection of scripts written by members of Junhyong Kim Lab at University of Pennsylvania. Its goal is to facilitate fast and interactive RNA-Seq data analysis and visualization. Current version of PIVOT supports routine RNA-Seq data analysis including normalization, differential expression analysis, dimension reduction, correlation analysis, clustering and classification. Users can complete workflows of DESeq2, monocle and scde package with just a few button clicks. All analysis reports can be exported, and the program state can be saved, loaded and shared.


PIVOT is installable as a regular R package or a (currently) community-developed Docker image

Regular installation

  • Main Program: Please copy and paste the following command to R console.
  • Upgrading R and Rstudio to the latest version (R >= 3.4, Rstudio > 1.0.0) is strongly recommended.
# Dependecies that needs to be manually installed.
# You may need to paste the following code line by line 
# and choose if previously installed packages should be updated (recommended).


# Install PIVOT
BiocManager::install("BiocGenerics") # You need the latest BiocGenerics >=0.23.3

Running PIVOT from regular install

  • To run PIVOT, in Rstudio console, use command

Docker installation

PIVOT can be launched (and installed/updated if needed) using a single command:

docker pull eturkes/pivot-docker && docker run -p 80:3838 eturkes/pivot-docker
  • Confirmation that PIVOT is running is identified by the line Listening on in the terminal window.
  • Once running, PIVOT can be visited in a web brower browser at the address localhost.
  • When finished, the Docker instance can be closed by entering Ctrl+C into its terminal window. Note that the PIVOT state will NOT be saved, one must use the "Save State" feature within "System Control" (section 17 of the PIVOT manual) if they would like to do so.

User manual

See here:

Or download:


For now, we suggest only load necessary modules when launching PIVOT. If your analysis require entire workflow, consider adding the environmental variable "R_MAX_NUM_DLLS=150" to .Renviron file located at "/Library/Frameworks/R.framework/Resources/etc"(MacOs); or .bash_profile with "export R_MAX_NUM_DLLS=150" (Linux).

  • 'SingleCellExperiment' package cannot be correctly installed
    • Please update your bioconductor to the latest version (>=3.6) and retry installation using the following command:
  • If you ran into any problems like 'SingleCellExperiment','SCESet' or 'pData', its likely that you have old scater installed. The new scater package changes all the grammar so you need to first remove the old package by calling remove.packages("scater") and reinstall the latest version by using BiocManager::install("scater").

  • Dependency openssl configuration failed

    • Linux: Please install the latest libgdal-dev package (apt-get install libgdal-dev)
    • MacOS: Please install brew ( first, then in terminal brew install openssl. Then try install PIVOT again.
  • MacOS specific: you might need to install xcode developer tools if you encounter installation error such as 'missing xcrun'.

To install, Open Terminal, and run the following:

xcode-select --install

  • If a dependency fails to install, try installing the package separately using BiocManager::install if it is from BioConductor or install.packages() if it is CRAN (if you are unsure, try one and if it fails, try the other). Some users found this was necessary for the BioConductor packages GenomicAlignments and rtracklayer. If the package still fails to install, you can try binaries from BioConductor/CRAN or package manager if on Linux. Some users found this was necessary for the CRAN package nloptr.


  • Zhu, Q., Fisher, S. A., Dueck, H., Middleton, S., Khaladkar, M., & Kim, J. (2018). PIVOT: platform for interactive analysis and visualization of transcriptomics data. BMC bioinformatics, 19(1), 6.

  • For specific analysis, please check the citation listed in the module.

Qin Zhu

Junhyong Kim Lab

University of Pennsylvania

2015 - 2017

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